tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911370339942400 |
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| author | Baumer, Benjamin S. Sierra, Biviana Marcela Suarez |
| author_facet | Baumer, Benjamin S. Sierra, Biviana Marcela Suarez |
| contents | We present tidychangepoint, a new R package for changepoint detection analysis. Most R packages for segmenting univariate time series focus on providing one or two algorithms for changepoint detection that work with a small set of models and penalized objective functions, and all of them return a custom, nonstandard object type. This makes comparing results across various algorithms, models, and penalized objective functions unnecessarily difficult. tidychangepoint solves this problem by wrapping functions from a variety of existing packages and storing the results in a common S3 class called tidycpt. The package then provides functionality for easily extracting comparable numeric or graphical information from a tidycpt object, all in a tidyverse-compliant framework. tidychangepoint is versatile: it supports both deterministic algorithms like PELT (from changepoint), and also flexible, randomized, genetic algorithms (via GA) that -- via new functionality built into tidychangepoint -- can be used with any compliant model-fitting function and any penalized objective function. By bringing all of these disparate tools together in a cohesive fashion, tidychangepoint facilitates comparative analysis of changepoint detection algorithms and models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_14369 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series Baumer, Benjamin S. Sierra, Biviana Marcela Suarez Methodology Computation 62P99 G.3 We present tidychangepoint, a new R package for changepoint detection analysis. Most R packages for segmenting univariate time series focus on providing one or two algorithms for changepoint detection that work with a small set of models and penalized objective functions, and all of them return a custom, nonstandard object type. This makes comparing results across various algorithms, models, and penalized objective functions unnecessarily difficult. tidychangepoint solves this problem by wrapping functions from a variety of existing packages and storing the results in a common S3 class called tidycpt. The package then provides functionality for easily extracting comparable numeric or graphical information from a tidycpt object, all in a tidyverse-compliant framework. tidychangepoint is versatile: it supports both deterministic algorithms like PELT (from changepoint), and also flexible, randomized, genetic algorithms (via GA) that -- via new functionality built into tidychangepoint -- can be used with any compliant model-fitting function and any penalized objective function. By bringing all of these disparate tools together in a cohesive fashion, tidychangepoint facilitates comparative analysis of changepoint detection algorithms and models. |
| title | tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series |
| topic | Methodology Computation 62P99 G.3 |
| url | https://arxiv.org/abs/2407.14369 |